Biorthogonal wavelet based entropy feature extraction for identification of maize leaf diseases

Crop disease is considered as a major constraint to both food quality and production. Even in this era of precision agriculture, the lacking of compulsory infrastructure has made rapid identification of crop diseases quite hard in numerous regions around the world. In this paper, we introduced a new method based on biorthogonal wavelet transform (BWT) to identify prime maize leaf diseases. We performed biorthogonal wavelet decomposition and pixel wise morphological operation to segment the maize leaf lesion from input image. For feature extraction, by applying 2-D biorthogonal wavelet transform (BWT) at multiple levels we proposed a novel method to extract colour channel wise wavelet entropy features by investigating discriminatory potential of three different biorthogonal wavelet filters (bior3.3, bior3.5, and bior3.7). The effectiveness of our extracted features were evaluated by employing five different classifiers and obtaining 95.78% overall identification accuracy with 10-fold cross validation. All the materials related our study can be found at: https://github.com/BadhanMazumder/BiorthogonalWavelet_MaizeDiseaseDetection.git. Schematic diagram of our proposed maize leaf disease identification approach. [Display omitted] •Developing a biorthogonal wavelet transform (BWT) based method to detect and classify prime maize leaf disease.•An effective maize leaf lesion segmentation algorithm is introduced by implementing BWT and morphological operations.•Introducing a feature extraction algorithm for maize leaf disease classification by using BWT with three wavelet filters.•Obtaining a higher classification accuracy rate to determine the suitable classification scheme for the extracted features.